**2. NILM State-of-the-Art Review**

Considering current and voltage acquisition and processing, the literature on NILM can be divided into two main categories—"high-frequency" and "low-frequency". The low-frequency is the category in which the features are extracted at 1 kHz or less. The high-frequency is the category in which features are extracted at kHz or MHz [15,19]. Table 1 presents a list of NILM studies and the features extracted from the point of view of the attributes.

**Table 1.** Summary and categorization of some Non–Intrusive Load Monitoring (NILM) techniques— relevant works and their main features.


**Note:** *P*: active power; *Q*: reactive power; *IRMS*: effective current; *URMS*: effective voltage; *UMAX*: maximum voltage; *IMAX*: maximum current; *PF*(*λ*): power factor; *PON load*: load on probability; *POFF load* : load off probability; *Ppulse*(*t*): time power pulse; *<sup>H</sup>*1,3,5,··· ,*N*: *n*th harmonic components (*n* = 1, 3, ..., *N*); *THD*: voltage or current total harmonic distortion; *S* or *A*: apparent power; *H*: harmonic power; *If* : inactive current; *CPTcomponents*: CPT power components; *Wcoef* : wavelet coefficients; *We*: wavelet equivalent coefficients; *V Itraj*: voltage vs current trajectory; *Hf* : non-fundamental power components; *<sup>i</sup>*(*t*): instantaneous current; *p*(*t*): instantaneous power.

In 1992, Hart [14] did some pioneering work on load disaggregation, in which he defined Nonintrusive Appliance Load Monitoring (NALM)—NILM is a derivative term from NALM. Such work showed that it is possible to separate power consumption by appliances observing the collective power consumption. To do this, it is necessary to discover the power behavior of each load or appliance. For a long time, this research did not draw attention due to digital device limitations for embedded algorithms. At the same time, Sultanem [40] proposed a different algorithm to Hart's work and used the PQ trajectory with harmonic decomposition.

Considering the P-Q (active and reactive power) analysis, Drenker and Kader [26] validated their NILM method with six loads that operate in steady mode, obtaining an accuracy of around 95%. Cole and Albicki [27] studied the steady-state loads and some loads with slow power changing (such as heat pump compressors) and they considered the total consumption of each load as geometrical shape forms. Norford and Leeb [28] also detected transient status of some loads and created an approximation for these transient statuses. Biansoongnem and Plungklang [29] created a NILM method with 90% of accuracy when testing air conditioning and refrigerators. Using deep learning to detect operational load changes, Xiao and Cheng proposed a method and validated it using the Reference Energy Disaggregation Dataset (REDD) [59].

Powers et al. [31] applied a rule-based algorithm to detect loads with high consumption, such as air conditioning, water heaters and electric space heaters. Likewise, rule-based algorithms were proposed by Farinaccio and Zmeureanu [32] to detect power load behavior, as well as by Marceau and Zmeureanu [33] with the indication of 90% accuracy. With regards to genetic algorithms, Baranski and Voss [34] proposed their employment to detect patterns based on the use of loads frequency.

Ruzzelli et al. [35] created a dataset with low-frequency features of voltages and currents (*IRMS*, *VRMS*, *IMAX*, *VMAX*) and the power factor (PF) to describe load behavior, and with the P-Q analysis they carried out load disaggregation. Kelly and Knottenbelt [36] used a deep neural network for load disaggregation, as well as the UK Domestic Appliance-Level Electricity (UK-DALE) dataset [60], achieving an excellent performance for that dataset. Figueiredo et al. [37] used the load step changes in active power and the PF to create a dataset and concluded that there is a need to extract other attributes that can better detail the loads, especially those that have the same power and the same behavior as the equivalent circuit.

Kim et al. [38] combined frequency independent features, such as the distribution probability of ON/OFF duration, frequency of appliance usage and the correlation between the usage of various appliances with the active power feature, achieving between 64% to 99.8% accuracy in terms of load disaggregation. Koutitas and Tassiulas [39] replaced time-series power analysis for a set of discrete pulses. They created features based on pulses, such as variance, spike, slope, periodicity, multi-state, and sequence of operation. They reached an accuracy of 85%.

Sultanem [40] is the pioneer of the high-frequency use on NILM applications. Srinivasan et al. [41] used machine learning to recognize the harmonic signatures of 8 loads, and they obtained an accuracy of 99% or more for the load disaggregation. Laughman et al. [42] used the P-Q analysis for similar loads and increased the 3rd harmonic to distinguish them. Bouhouras et al. [43] used harmonic components to create a dataset for load disaggregation with stand-alone loads or combined loads, and the accuracy was between 85% and 95%.

Dong et al. [7] adopted Total Harmonic Distortion (THD) of current waveforms and P-Q analysis for load discrimination, and they used some pulse-based features. Lin et al. [17] created a NILM using features including the THD, P-Q analysis, voltage-current trajectory, current indicators and quadratic programming. The accuracy of this work is generally more than 90%.

Using power theory concepts, Teshome et al. [44] proposed a NILM with components of active, reactive, apparent power, and nonactive currents. Nguyen et al. [45] created a NILM method based on active, reactive and apparent power and used a decision tree (DT) to disaggregate five loads, achieving more than 98.8% accuracy. Huang et al. [46] pointed out that the application of power theories can be a useful tool for load disaggregation, especially for loads that have the same value of active power. In 2003, Tenti and Mattavelli [23,24] proposed the Conservative Power Theory, which allows load modelling in terms of power components. This work was the basis for the load characterization in the NILM dataset proposed by Souza et al. [15,25].

Considering the Continuous Wavelet Transform (CWT), Chan et al. [47] applied it up to the 4th level for the loads, and used *Daubechies* as mother wavelets, achieving an accuracy of 70%. Su et al. [48], and Duarte et al. [49] compared the Fast Fourier Transform (FFT) with CWT, pointing out the advantages of the CWT during load transients and recommended using CWT for feature extraction for load disaggregation. Chang et al. [52,53] implemented a NILM using active and reactive power with CWT. The authors extracted load features from five filters and applied a genetic algorithm for load identification. They reached almost 100% accuracy but the studies were carried out considering situations of significant discrepancy in power levels.

Gray and Morsi [50] used time-consuming energy to obtain CWT decomposition with Daubechies mother wavelets. The authors presented results comparing the accuracy of applying each order of the Daubechies order and concluded that the higher the Daubechie, the greater the accuracy. Tabatabaei [51] did a similar study, but used power characteristics to create classificatory features and obtained accuracy at around 85%. Gillis et al. [19] proposed a new CWT for NILM applications, obtaining around 94% accuracy for four connected loads at the same time.

Hassan et al. [54] created a NILM method based on V-I trajectory, which used wave-shape features along with the REDD dataset. Similarly, V-I trajectories were mapped to a grid of cells (as a matrix) by Du et al. [18], having a binary value assigned to each of them. On the other hand, 83% of accuracy was achieved by Gao et al. [55] by converting V-I trajectory into a binary image, while using combined features, and considering 11 appliances. Finally, convolutional neural networks were proposed to be used with V-I trajectory by Baets et al. [56], reaching 77.6% of accuracy for the PLAID dataset [61], and 75.46% for the WHITED dataset [62]. Voltage harmonic (FFT) noise has also been used by Patel et al. [57,58], taking into account noise and electromagnetic interference in the range of 36 to 500 kHz. Nonetheless, such a study only highlighted the types of equipment that present multiple operational stages.

To summarize, there are many other studies regarding NILM with different methodologies, feature extraction, different appliances in the validation and different load disaggregation algorithms. Nevertheless, in Teshome et al. [44], the authors indicate the importance of modern power theories and the lack of these elegant circuit analyses to improve the NILM systems. One of these elegant power theories pointed out in Teshome et al. [44] was the CPT. Thus, such a modern power theory is applied in this work to improve the load disaggregation and present a novel NILM technique.

Accordingly, the next section presents the PSB, a new NILM methodology based on a state machine, which analyzes the active power signature (a low-frequency feature), and on the event detection, which finds features from the CPT and triggers the machine learning algorithm that uses the high-frequency attribute dataset proposed by Souza et al. [15].

### **3. The Power Signature Blob Method**

### *3.1. Dataset with the Microscopic Features Extraction*

In Souza et al. [15] some techniques for appliance disaggregation were evaluated, and the feasibility of identifying home appliances using pattern recognition algorithms was shown. Two pattern recognition algorithms achieved significant results: Optimum-Path Forest (OPF) [63] and K-Nearest Neighbor (KNN) [64] and the KNN (with *K* = 1) was chosen because of its lower computational time. The voltage and current waveforms from several appliances were measured and decomposed in power components using CPT [23,24]. The CPT allows splitting the power into active, reactive, unbalance and residual parts. These power components help to interpret an appliance as an equivalent circuit, as shown in Figure 1, where *vm* is the phase voltage, the current *iGm* coincides with the active current, *iLm* coincides with the reactive current, and the current source *jm* coincides with the void current. *Gm* is the equivalent phase conductance and could be represented as a resistance, *Lm* is the equivalent phase inductance and could be represented as an inductor. All the mathematical background of the equivalent circuit can be found in Reference [65].

**Figure 1.** Load equivalent circuit by Conservative Power Theory (CPT).

Using the CPT power decomposition, Souza et al. [15] created a dataset of 35 home appliances such as irons, microwaves, refrigerators, washing machines, lamps and others. Each appliance features (CPT active power, power factor, reactivity factor, and nonlinearity factor [25]) refer to a set dimension and each collected instance as a point in the multidimensional space. The pattern recognition algorithm uses this dataset for the classification purpose of the appliance.

Figure 2 shows the Voronoi diagram concerning the 1NN results for the appliance dataset of [15], where each class number represents an appliance. It shows three Voronoi diagrams for the four attributes (P—Active power, PF—power factor, QF—reactivity factor, and VF—nonlinearity factor) from the dataset, presented in two dimensions to help visualize the decision boundaries.

**Figure 2.** K-Nearest Neighbor (KNN) decision boundaries of the CPT appliance dataset.

The supervised classification algorithm identifies which appliances consume electricity, according to CPT power terms calculation. However, it is likely that some false positives could be detected (when the appliance is identified mistakenly due to the similarity with others, for example, a 100 W bulb lamp and a 100 W LCD TV with high power factor). Another potential issue concerns some appliances with multiple power stages during the power operation, such as washing machines (with washing, spinning and rinsing). This occurs because the classifier needs to observe various levels of "ON" and "OFF" and could not perform the correct classification. Some methodologies [7,8,10,16,22,37,42,66] were created to solve the multiple power steps problem, and both observe the appliance behavior during time operation. The load power signature is relevant because some appliances do not operate in steady-state, and the method needs to disaggregate with high accuracy. Thus, in Reference [15] there was a microscopic feature dataset for the load classification, but it is required to create a method to observe the appliance power behavior before using the appliance classification.
